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Massively Parallel Single Nucleus Transcriptional Profiling Defines Spinal Cord Neurons and Their Activity during Behavior.
To understand the cellular basis of behavior, it is necessary to know the cell types that exist in the nervous system and their contributions to function. Spinal networks are essential for sensory processing and motor behavior and provide a powerful system for identifying the cellular correlates of behavior. Here, we used massively parallel single nucleus RNA sequencing (snRNA-seq) to create an atlas of the adult mouse lumbar spinal cord. We identified and molecularly characterized 43 neuronal populations. Next, we leveraged the snRNA-seq approach to provide unbiased identification of neuronal populations that were active following a sensory and a motor behavior, using a transcriptional signature of neuronal activity. This approach can be used in the future to link single nucleus gene expression data with dynamic biological responses to behavior, injury, and disease.
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ArrayExpress update - from bulk to single-cell expression data.
ArrayExpress (https://www.ebi.ac.uk/arrayexpress) is an archive of functional genomics data from a variety of technologies assaying functional modalities of a genome, such as gene expression or promoter occupancy. The number of experiments based on sequencing technologies, in particular RNA-seq experiments, has been increasing over the last few years and submissions of sequencing data have overtaken microarray experiments in the last 12 months. Additionally, there is a significant increase in experiments investigating single cells, rather than bulk samples, known as single-cell RNA-seq. To accommodate these trends, we have substantially changed our submission tool Annotare which, along with raw and processed data, collects all metadata necessary to interpret these experiments. Selected datasets are re-processed and loaded into our sister resource, the value-added Expression Atlas (and its component Single Cell Expression Atlas), which not only enables users to interpret the data easily but also serves as a test for data quality. With an increasing number of studies that combine different assay modalities (multi-omics experiments), a new more general archival resource the BioStudies Database has been developed, which will eventually supersede ArrayExpress. Data submissions will continue unchanged; all existing ArrayExpress data will be incorporated into BioStudies and the existing accession numbers and application programming interfaces will be maintained.
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omicsGAT: Graph Attention Network for Cancer Subtype Analyses.
The use of high-throughput omics technologies is becoming increasingly popular in all facets of biomedical science. The mRNA sequencing (RNA-seq) method reports quantitative measures of more than tens of thousands of biological features. It provides a more comprehensive molecular perspective of studied cancer mechanisms compared to traditional approaches. Graph-based learning models have been proposed to learn important hidden representations from gene expression data and network structure to improve cancer outcome prediction, patient stratification, and cell clustering. However, these graph-based methods cannot rank the importance of the different neighbors for a particular sample in the downstream cancer subtype analyses. In this study, we introduce omicsGAT, a graph attention network (GAT) model to integrate graph-based learning with an attention mechanism for RNA-seq data analysis. The multi-head attention mechanism in omicsGAT can more effectively secure information of a particular sample by assigning different attention coefficients to its neighbors. Comprehensive experiments on The Cancer Genome Atlas (TCGA) breast cancer and bladder cancer bulk RNA-seq data and two single-cell RNA-seq datasets validate that (1) the proposed model can effectively integrate neighborhood information of a sample and learn an embedding vector to improve disease phenotype prediction, cancer patient stratification, and cell clustering of the sample and (2) the attention matrix generated from the multi-head attention coefficients provides more useful information compared to the sample correlation-based adjacency matrix. From the results, we can conclude that some neighbors play a more important role than others in cancer subtype analyses of a particular sample based on the attention coefficient.
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Single-cell RNA-sequencing analysis of the ciliary epithelium and contiguous tissues in the mouse eye.
The ciliary epithelium plays a central role in ocular homeostasis but cells of the pigmented and non-pigmented layers are difficult to isolate physically and study. Here we used single-cell RNA-sequencing (scRNA-seq) to analyze the transcriptional signatures of cells harvested from the ciliary body and contiguous tissues. Microdissected tissue was dissociated by collagenase digestion and the transcriptomes of individual cells were obtained using a droplet-based scRNA-seq approach. In situ hybridization was used to verify the expression patterns of selected differentially-expressed genes. High quality transcriptomes were obtained from 10,024 cells and unsupervised clustering distinguished 22 cell types. Although efforts were made to specifically isolate the ciliary body, approximately half of the sequenced cells were derived from the adjacent retina. Cluster identities were assigned using expression of canonical markers or cluster-specific genes. The transcriptional signature of cells in the PCE and NPCE were distinct from each other and from cells in contiguous tissues. PCE cell transcriptomes were characterized by genes involved in melanin synthesis and transport proteins such as Slc4a4. Among the most differentially expressed genes in NPCE cells were those encoding members of the Zic family of transcription factors (Zic1, 2, 4), collagen XVIII (Col18a1), and corticotrophin-releasing hormone-binding protein (Crhbp). The ocular melanocyte population was distinguished by expression of the gap junction genes Gjb2 and Gjb6. Two fibroblast signatures were detected in the ciliary body preparation and shown by in situ hybridization to correspond to uveal and scleral populations. This cell atlas for the ciliary body and contiguous layers represents a useful resource that may facilitate studies into the development of the ciliary epithelium, the production of the aqueous and vitreous humors, and the synthesis of the ciliary zonule.
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CELLama: Foundation Model for Single Cell and Spatial Transcriptomics by Cell Embedding Leveraging Language Model Abilities.
Large-scale single-cell RNA sequencing (scRNA-seq) and spatial transcriptomics (ST) have transformed biomedical research into a data-driven field, enabling the creation of comprehensive atlases. These methodologies facilitate detailed understanding of biology and pathophysiology; however, the complexity and sheer volume of data present analytical challenges, particularly in robust cell typing, integration, and understanding complex spatial relationships of cells. To address these challenges, CELLama (Cell Embedding Leverage Language Model Abilities) develops a framework that leverage language model to transform cell data into "sentences" that encapsulate gene expressions and metadata, enabling universal cell embedding. CELLama, serving as a foundation model, supports flexible applications ranging from cell typing to analysis of spatial contexts, independent of complex dataset-specific analysis workflows by using a large cell atlas. The results demonstrate that CELLama has significant potential to transform cellular analysis in various contexts, from determining cell types using multi-tissue atlases and their interactions to unraveling intricate tissue dynamics.
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Artificial Intelligence Revolution in Transcriptomics: From Single Cells to Spatial Atlases.
Single-cell RNA sequencing (scRNA-seq) and spatial transcriptomics (ST) have revolutionized the study of cellular heterogeneity and tissue organization. However, the increasing scale and complexity of these data demand more powerful and integrative computational strategies. Although conventional statistical and machine learning methods remain effective in specific contexts, they face limitations in scalability, multimodal integration, and generalization. In response, artificial intelligence (AI) has emerged as a transformative force, enabling new modes of analysis and interpretation. In this review, we survey AI applications across the transcriptomic analysis workflow-from initial preprocessing through key downstream analyses such as trajectory inference, gene regulatory network reconstruction, and spatial domain detection. For each analytical task, we trace the developmental trajectory and evolving trends of AI models, summarize their advantages, limitations, and domain-specific applicability. We also highlight key innovations, ongoing challenges, and future directions. Furthermore, this review provides practical guidance to assist researchers in model selection and support developers in the design of novel AI tools. An online companion supplement providing an in-depth look at all methods discussed: https://zhanglab-kiz.github.io/review-ai-transcriptomics.
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M2 macrophage marker CHI3L2 could serve as a potential prognostic and immunological biomarker in glioma by integrated single-cell and bulk RNA-Seq analysis.
BACKGROUND: CHI3L2 plays a crucial role in multiple cancers, but its importance in glioma remains unclear. Hence, we comprehensively integrated bulk RNA-sequencing (RNA-seq), proteomics and single-cell RNA-seq (scRNA-seq) to determine the roles of CHI3L2 in gliomas. METHODS: Bulk RNA-seq, proteomics and scRNA-seq data of CHI3L2 in glioma were obtained from online databases. The quantitative real-time polymerase chain reaction (qRT-PCR) and immunohistochemistry (IHC) were conducted to verify the CHI3L2 expression. Then, univariate and multivariate Cox regression analyses, Norman charts and gene set enrichment analysis (GSEA) were performed. Finally, the associations between CHI3L2 and tumor immunity were explored. RESULTS: The expression of CHI3L2 was markedly higher in glioma cancers compared with normal tissues from analysis of the data of the Cancer Genome Atlas and Chinese Glioma Genome Atlas datasets and as verified by GSE4290, GSE50161, qRT-PCR and IHC results (p < 0.05). High expression of CHI3L2 suggested poor overall survival (OS) prognosis in gliomas (p < 0.05). CHI3L2 might also serve as an independent predictor of OS for gliomas (p < 0.05) and we also constructed a Norman chart to predict these patients' survival prognosis with good performance. GSEA analysis showed that CHI3L2 might be involved with eight pathways in gliomas. Regarding tumor immunity, CHI3L2 was found to be significantly involved with immune cell infiltration levels of low-grade glioma, the tumor immune microenvironment, immune checkpoints and immune cells in both low-grade glioma and glioblastoma (p < 0.05). Additionally, scRNA-seq data for CHI3L2 in glioma from the TISCH2 website showed that CHI3L2 is mainly expressed in astrocytes, endothelial cells, CD8+ T cells, mono/macrophage cells, etc. CONCLUSIONS: CHI3L2 presents prognostic and immunological values in glioma, providing novel therapeutic targets for glioma patients.
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A single cell atlas of human cornea that defines its development, limbal progenitor cells and their interactions with the immune cells.
PURPOSE: Single cell (sc) analyses of key embryonic, fetal and adult stages were performed to generate a comprehensive single cell atlas of all the corneal and adjacent conjunctival cell types from development to adulthood. METHODS: Four human adult and seventeen embryonic and fetal corneas from 10 to 21 post conception week (PCW) specimens were dissociated to single cells and subjected to scRNA- and/or ATAC-Seq using the 10x Genomics platform. These were embedded using Uniform Manifold Approximation and Projection (UMAP) and clustered using Seurat graph-based clustering. Cluster identification was performed based on marker gene expression, bioinformatic data mining and immunofluorescence (IF) analysis. RNA interference, IF, colony forming efficiency and clonal assays were performed on cultured limbal epithelial cells (LECs). RESULTS: scRNA-Seq analysis of 21,343 cells from four adult human corneas and adjacent conjunctivas revealed the presence of 21 cell clusters, representing the progenitor and differentiated cells in all layers of cornea and conjunctiva as well as immune cells, melanocytes, fibroblasts, and blood/lymphatic vessels. A small cell cluster with high expression of limbal progenitor cell (LPC) markers was identified and shown via pseudotime analysis to give rise to five other cell types representing all the subtypes of differentiated limbal and corneal epithelial cells. A novel putative LPCs surface marker, GPHA2, expressed on the surface of 0.41% ± 0.21 of the cultured LECs, was identified, based on predominant expression in the limbal crypts of adult and developing cornea and RNAi validation in cultured LECs. Combining scRNA- and ATAC-Seq analyses, we identified multiple upstream regulators for LPCs and demonstrated a close interaction between the immune cells and limbal progenitor cells. RNA-Seq analysis indicated the loss of GPHA2 expression and acquisition of proliferative limbal basal epithelial cell markers during ex vivo LEC expansion, independently of the culture method used. Extending the single cell analyses to keratoconus, we were able to reveal activation of collagenase in the corneal stroma and a reduced pool of limbal suprabasal cells as two key changes underlying the disease phenotype. Single cell RNA-Seq of 89,897 cells obtained from embryonic and fetal cornea indicated that during development, the conjunctival epithelium is the first to be specified from the ocular surface epithelium, followed by the corneal epithelium and the establishment of LPCs, which predate the formation of limbal niche by a few weeks. CONCLUSIONS: Our scRNA-and ATAC-Seq data of developing and adult cornea in steady state and disease conditions provide a unique resource for defining genes/pathways that can lead to improvement in ex vivo LPCs expansion, stem cell differentiation methods and better understanding and treatment of ocular surface disorders.
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Single cell dissection of early kidney development: multilineage priming.
We used a single cell RNA-seq strategy to create an atlas of gene expression patterns in the developing kidney. At several stages of kidney development, histologically uniform populations of cells give rise to multiple distinct lineages. We performed single cell RNA-seq analysis of total mouse kidneys at E11.5 and E12.5, as well as the renal vesicles at P4. We define an early stage of progenitor cell induction driven primarily by gene repression. Surprising stochastic expression of marker genes associated with differentiated cell types was observed in E11.5 progenitors. We provide a global view of the polarized gene expression already present in the renal vesicle, the first epithelial precursor of the nephron. We show that Hox gene read-through transcripts can be spliced to produce intergenic homeobox swaps. We also identify a surprising number of genes with partially degraded noncoding RNA. Perhaps most interesting, at early developmental times single cells often expressed genes related to several developmental pathways. This provides powerful evidence that initial organogenesis involves a process of multilineage priming. This is followed by a combination of gene repression, which turns off the genes associated with most possible lineages, and the activation of increasing numbers of genes driving the chosen developmental direction.
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Prognostic Implication of the Expression Level of PECAM-1 in Non-small Cell Lung Cancer.
Background: Lung cancer is a malignant disease that threatens human health. Hence, it is crucial to identify effective prognostic factors and treatment targets. Single-cell RNA sequencing can quantify the expression profiles of transcripts in individual cells. Methods: GSE117570 profiles were downloaded from the Gene Expression Omnibus database. Key ligand-receptor genes in the tumor and the normal groups were screened to identify integrated differentially expressed genes (DEGs) from the GSE118370 and The Cancer Genome Atlas Lung Adenocarcinoma databases. DEGs associated with more ligand-receptor pairs were selected as candidate DEGs for Gene Ontology (GO) functional annotation, Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analysis, and survival analysis. In addition, we conducted validation immunohistochemical experiments on postoperative specimens of 30 patients with lung cancer. Results: A total of 18 candidate DEGs were identified from the tumor and the normal groups. The analysis of the GO biological process revealed that these DEGs were mainly enriched in wound healing, in response to wounding, cell migration, cell motility, and regulation of cell motility, while the KEGG pathway analysis found that these DEGs were mainly enriched in proteoglycans in cancer, bladder cancer, malaria, tyrosine kinase inhibitor resistance in Epidermal Growth Factor Receptor (EGFR), and the ERBB signaling pathway. Survival analysis showed that a high, rather than a low, expression of platelet endothelial cell adhesion molecule-1 (PECAM-1) was associated with improved survival. Similarly, in postoperative patients with lung cancer, we found that the overall survival of the PECAM-1 high-expression group shows a better trend than the PECAM-1 low-expression group (p = 0.172). Conclusions: The candidate DEGs identified in this study may play some important roles in the occurrence and development of lung cancer, especially PECAM-1, which may present potential prognostic biomarkers for the outcome.